Dry-spell assessment through rainfall downscaling comparing deep-learning algorithms and conventional statistical frameworks

By Panagiotis Mavritsakis 

Large parts of the world rely on rainfed agriculture for their food security. In Africa, 90% of the agricultural yields rely only on precipitation for irrigation purposes and approximately 80% of the population’s livelihood is highly dependent on its food production. Parts of Ghana are prone to droughts and flood events due to increasing variability of precipitation phenomena. Crop growth is sensitive to the wet- and dry-spell phenomena during the rainy season. To support rural communities and small farmer in their efforts to adapt to climate change and natural variability, it is crucial to have good predictions of rainfall and related dry/wet spell indices.

This research constitutes an attempt to assess the dry-spell patterns in the northern region of Ghana, near Burkina Faso. We aim to develop a model which by exploiting satellite products overcomes the poor temporal and spatial coverage of existing ground precipitation measurements. The main objective is to reproduce the dry spell sequences as seen by the rain gauges (point scale) in the region of Northern Ghana based on satellite precipitation products (CMORPH, TAMSAT, IMERG).
We will compare conventional statistical tools and Machine Learning classification models and deep-learning algorithms to establish a link between satellite products and field rainfall data for dry-spell assessment. The deep-learning architecture used should be able to process satellite images efficiently. Hence, several Convolutional Neural Network architectures were tested as classifiers.

Using these models we will attempt to exploit the long temporal coverage of the satellite products in order to overcome the poor temporal and spatial coverage of existing ground precipitation measurements. Doing that, our final objective is to enhance our knowledge about the dry-spell characteristics and, thus, provide more reliable climatic information to the smallholder farmers in the area of Northern Ghana.